4056 • The Journal of Neuroscience, April 6, 2016 • 36(14):4056 – 4066
Behavioral/Cognitive
Dynamics of the Human Structural Connectome Underlying
Working Memory Training
Karen Caeyenberghs,1* X Claudia Metzler-Baddeley,2* Sonya Foley,2 and Derek K. Jones2
1
School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria 3065, Australia, and 2Cardiff University Brain
Research Imaging Institute, School of Psychology, and Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff CF10 3AT, United
Kingdom
Brain region-specific changes have been demonstrated with a variety of cognitive training interventions. The effect of cognitive training
on brain subnetworks in humans, however, remains largely unknown, with studies limited to functional networks. Here, we used a
well-established working memory training program and state-of-the art neuroimaging methods in 40 healthy adults (21 females, mean
age 26.5 years). Near and far-transfer training effects were assessed using computerized working memory and executive function tasks.
Adaptive working memory training led to improvement on (non)trained working memory tasks and generalization to tasks of reasoning
and inhibition. Graph theoretical analysis of the structural (white matter) network connectivity (“connectome”) revealed increased
global integration within a frontoparietal attention network following adaptive working memory training compared with the nonadaptive group. Furthermore, the impact on the outcome of graph theoretical analyses of different white matter metrics to infer “connection
strength” was evaluated. Increased efficiency of the frontoparietal network was best captured when using connection strengths derived
from MR metrics that are thought to be more sensitive to differences in myelination (putatively indexed by the [quantitative] longitudinal
relaxation rate, R1 ) than previously used diffusion MRI metrics (fractional anisotropy or fiber-tracking recovered streamlines). Our
findings emphasize the critical role of specific microstructural markers in providing important hints toward the mechanisms underpinning training-induced plasticity that may drive working memory improvement in clinical populations.
Key words: cognitive control; connectome; diffusion MRI; graph analysis; memory training; structural MRI
Significance Statement
This is the first study to explore training-induced changes in the structural connectome using a well-controlled design to examine
cognitive training with up-to-date neuroimaging methods. We found changes in global integration based on white matter connectivity within a frontoparietal attention network following adaptive working memory training compared with a nonadaptive
comparison group. Furthermore, the impact of different diffusion MR metrics and more specific markers of white matter on the
graph theoretical findings was evaluated. An increase in network global efficiency following working memory training was best
captured when connection strengths were weighted by MR relaxation rates (influenced by myelination). These results are important for the optimization of cognitive training programs for healthy individuals and people with brain disease.
Introduction
Graph theory is a powerful mathematical framework for quantifying topological properties of networks (Sporns, 2014). In recent
years, it has emerged as a useful tool for characterizing brain
Received May 22, 2015; revised Jan. 28, 2016; accepted Feb. 4, 2016.
Author contributions: K.C., C.M.B., and D.K.J. designed research; K.C., C.M.B., S.F., and D.K.J. performed research;
K.C., C.M.B., S.F., and D.K.J. analyzed data; K.C., C.M.B., and D.K.J. wrote the paper.
This work was supported by a Wellcome Trust New Investigator Award to D.K.J., K.C. was supported by a Research
Foundation (Flanders) travel grant. We thank Cyril Charron (Cardiff) for assistance with scripting the CHARMED
analysis pipeline; Sonya Bells (Cardiff) for assistance with the mcDESPOT processing pipeline and the Elastix coregistration; Adam Hampshire (London) for the provision of the cognitive benchmark tests; and Hadi Hosseini (Stanford
University) for help with the longitudinal plugin of the GAT toolbox.
The authors declare no competing financial interests.
*K.C. and C.M.B. contributed equally to this work.
network (“connectome”) changes during development, maturation, and aging (Collin and van den Heuvel, 2013). Life-span
connectome changes appear to follow an inverted U-shaped pattern, with an increasingly integrated topology during development, a plateau during adulthood, and an increasingly localized
This article is freely available online through the J Neurosci Author Open Choice option.
Correspondence should be addressed to Dr. Karen Caeyenberghs, School of Psychology, Faculty of Health
Sciences, Australian Catholic University, 115 Victoria Pde., Melbourne, Victoria 3065, Australia. E-mail:
[email protected].
DOI:10.1523/JNEUROSCI.1973-15.2016
Copyright © 2016 Caeyenberghs, Metzler-Baddeley et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License
Creative Commons Attribution 4.0 International, whichpermitsunrestricteduse,distributionandreproductioninany
medium provided that the original work is properly attributed.
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
J. Neurosci., April 6, 2016 • 36(14):4056 – 4066 • 4057
Table 1. Summary of demographic variables and performance in working memory
and executive function benchmark tests of the two groups at baseline and results
of independent t testa
n
Age (years)
Females
Right-handed
Forwards digit span
Backwards digit span
Spatial span
Double trouble
Grammatical reasoning
Tree task
Odd one out
Self-ordered spatial span
Automated symmetry span
No. of training sessions
Training
Controls
t(38)
p
20
26 (6.2)
11
19
5.3 (0.8)
4 (1.4)
5 (0.5)
22.8 (13.6)
0.79 (0.2)
23.7 (8.7)
9.5 (3.2)
6.2 (1.1)
25.3 (6.5)
40
20
27 (6.8)
10
20
5.2 (0.7)
4 (1.4)
4.9 (0.5)
25.9 (15.4)
0.73 (0.2)
19.8 (7.2)
9.1 (4.3)
5.5 (1.4)
22.6 (7.9)
40
—
0.44
—
—
⫺0.67
⫺0.01
⫺0.97
0.69
⫺0.97
⫺1.54
⫺0.37
⫺1.90
⫺1.18
—
—
0.67
—
—
0.51
0.99
0.34
0.49
0.34
0.13
0.71
0.07
0.25
—
Data are mean ⫾ SD.
a
Table 2. Rotated component matrix of the principal component analysis within the
complete dataset of the pretraining cognitive data (N ⴝ 46)a
Component
Cognitive task
1
2
3
Spatial span
Odd one out
Automated symmetry span
Double trouble
Digit span backwards
Grammatical reasoning
Digit span forwards
Hampshire tree
Self-ordered spatial span
0.761
0.738
0.648
⫺0.012
0.418
⫺0.03
0.26
0.196
0.128
0.067
0.028
0.256
0.777
0.688
0.551
0.543
0.017
0.276
0.355
0.03
0.085
⫺0.009
0.137
0.292
0.065
0.872
0.821
a
Rotation method: Varimax with Kaiser normalization.
topology in later life (Fair et al., 2008, 2009). These dynamic
changes are thought to contribute to critical changes in cognitive
ability across the life-span.
Brain plasticity can also be demonstrated over a much shorter
time period in response to cognitive training interventions (Taya
et al., 2015). For example, working memory training induces
changes in brain activity in frontal and parietal cortex (e.g.,
Klingberg, 2010; Jolles et al., 2013; Kundu et al., 2013; Olesen et
al., 2004). Attentional training produced increased resting cerebral blood flow in prefrontal cortex (Mozolic et al., 2010). Memory training modified cortical thickness in the right fusiform
gyrus and lateral orbitofrontal cortex in the elderly (Engvig et al.,
2010), whereas mental calculation training induced gray matter
volume changes in bilateral frontoparietal regions and left superior temporal gyrus (Takeuchi et al., 2011). Thus, there is ample
evidence of changes in specific, isolated brain regions in response
to various cognitive training interventions.
However, in examining plasticity, it is important to move beyond isolated brain regions and consider the impact of cognitive
training interventions on brain networks (Bressler and Menon,
2010). A network perspective is crucial in understanding the factors that drive training-induced changes, ultimately leading to
more effective treatment of neurological disorders. It is also important and necessary for capturing learning processes of cognitive functions that underpin training-induced changes in
cognition (Taya et al., 2015). This approach facilitates the integration of multiple sources of information, accounting for the
highly interconnected nature of brain, and is necessary because
intensive practice of higher-order cognitive skills (e.g., working
memory), may have a broader impact on neuronal integration,
which cannot be fully captured by analyzing single brain regions
or tracts. To date, only one study has used a connectome approach exploring training-induced brain changes (Langer et al.,
2013), albeit studying function (using fMRI). However, whether
and how intensive training activities produce measurable and
durable changes in structural brain integration is unclear.
Here, we investigated the effects of 2 months of adaptive
memory training and nonadaptive control activities on structural
networks. There is evidence from diffusion tensor MRI studies
that cognitive training can modify white matter fractional anisotropy (FA), a quantitative index of tissue microstructural organization (e.g., Scholz et al., 2009; Takeuchi et al., 2010; Wolf et al.,
2014). For example, working memory training has been shown
to result in increased FA in the intraparietal sulcus and anterior
corpus callosum (Takeuchi et al., 2010). Although frequently
concluded that increased myelination underpins traininginduced FA changes, FA can be modulated by a variety of biological factors, including myelination, packing density, and diameter
of the axonal fibers (Jones et al., 2013).
We examined for the first time white matter plasticity in 40
healthy adults with novel in vivo white matter imaging techniques
beyond diffusion tensor MRI, which offer enhanced specificity to
distinct attributes of white matter microstructure, such as myelination with MR relaxometry-based metrics (Deoni et al., 2008)
and axonal morphometrics (using advanced models of diffusion)
(Assaf and Basser, 2005). This study not only provides a compelling demonstration of structural network plasticity for the very
first time but also offers insight into the underlying mechanisms.
Materials and Methods
Participants. Forty-six healthy participants between the ages of 19 and 40
years were recruited from the Cardiff University School of Psychology
Community Panel and via poster advertisements in local shopping and
leisure centers. Participants were assigned to one of the two training
groups (adaptive or nonadaptive group) pseudo-randomly with the provision to match the groups for age, sex, and handedness (only one participant was left-handed). All participants were blind to their training
condition. All participants had normal or corrected vision, and none had
a history of neurological or psychiatric illness or reported recent drug or
alcohol abuse. All participants were carefully screened for MRI contraindications, such as pacemakers, metal contamination, or claustrophobia. Participants had to have a good command of the English language
and had to have access to a computer and internet connection at home to
be able to perform the working memory training (see below). All participants gave written informed consent to participate in this study under a
protocol approved by Ethics Committee of the School of Psychology at
Cardiff University. For various reasons, such as dropout and moving
away from the area, 6 participants were discarded from further analysis (3
from the adaptive and 3 from the nonadaptive group), leading to a total
of 40 remaining datasets (20 per group, 19 males and 21 females). The
two groups did not differ in terms of gender distribution, handedness, or
age (t(38) ⫽ 0.44, p ⫽ 0.67) (Table 1).
Training. Participants trained extensively for 8 weeks, ⬃45 min in each
session (40 sessions in total). Training was self-administered at home via
the software Cogmed RM. For full details and in-depth description of this
training program, the interested reader is referred to previous studies
(Klingberg et al., 2002; Astle et al., 2015) (or www.Cogmed.com/rm). In
brief, working memory capacity was trained with computerized exercises
of verbal (e.g., digits) and spatial (e.g., flashing lights) span tasks under
various conditions, such as repeating sequences in forwards or backwards order, repeating auditory verbal information with or without visual cues, repeating sequences of flashing lights in stationary or rotating
displays. In the high-capacity training condition, task difficulty increased
or decreased adaptively depending on the trainee’s level of performance.
The participants assigned to the nonadaptive training group trained on a
4058 • J. Neurosci., April 6, 2016 • 36(14):4056 – 4066
level of difficulty of three item spans independently of their performance
throughout the 40 training days.
Tasks for pretraining and post-training assessment. To assess near- and
far-transfer training effects, participants were assessed before and after
the training in a number of computerized working memory and executive function tasks from the Cambridge Brain Sciences Laboratory (www.
cambridgebrainsciences.com) (Owen et al., 2010).
Verbal and spatial working memory spans were assessed with computerized versions of the digit span forwards and backwards and the
spatial span task (Wechsler, 1999). In each version, the task difficulty
was adjusted by increasing the number of span items by one following
a successful trial and decreasing by one following an unsuccessful
trial. Outcome measures were the average number of digits or spatial
locations, respectively, in all successfully completed trials. Participants were allowed to make three errors in total before the task was
discontinued.
The ability to maintain and manipulate spatial information was assessed
with the self-ordered spatial span task (Owen et al., 2010). In this task, a
number of boxes appear on the screen and a token is hidden in one of the
boxes. Participants were instructed to find the token by clicking on the boxes
and to remember the location of the token because novel tokens were never
hidden in previously occupied boxes. Searching a box twice or clicking on a
box that previously contained a token was penalized and the task discontinued after three such errors. Participants completed a trial successfully when
all targets had been found with the outcome measures being the average
number of successfully completed trials.
The ability to suppress distracting and response conflicting information was assessed with the double trouble task, a version of the Stroop
task (Stroop, 1935). Participants were presented with a target color word
at the top and two response color words at the bottom of the screen and
were instructed to select with a mouse click the word that correctly described the target font color. Task difficulty was manipulated by varying
the congruency between the font color and color meaning of the target
and response words. The outcome measure was the number of correct
responses within 90 s.
Complex verbal reasoning was assessed with an adapted version of the
grammatical reasoning test (Baddeley, 1968), whereby participants have to
determine, as quickly as possible, whether grammatical statements (e.g., the
circle is not smaller than the square) about a presented figure (a large square
and a smaller circle) were correct or false and to complete as many trials as
possible within 90 s. The outcome measure was the total number of trials
answered correctly minus the number answered incorrectly.
Nonverbal abstract reasoning was assessed with the odd one out task,
an adaptation of the Raven’s Progressive Matrices (Raven, 1942), in
which participants are presented with nine patterns on the screen, each
made up of color, shape, and number features. Participants had to find
the one pattern that differed from the others according to a single feature
or a combination of features. The task difficulty increased with performance improvements. The outcome measure was the number of correctly solved trials within 3 min.
The ability to plan and think forward was assessed with the Hampshire
tree task, a version of the Tower of London/Hanoi test (Shallice, 1982).
Participants were presented with a tree-shaped frame with nine numbered balls slotted onto the branches and were instructed to rearrange the
balls so that they were ordered numerically with as few moves as possible.
Participants could only move one ball at a time and only move balls that
were not blocked by another ball. The time limit for this task was 3 min,
and the outcome measure was the number of correctly executed moves
with fewer moves reflecting better performance.
Finally, the ability to multitask was assessed with the automated symmetry span task (Unsworth et al., 2005), which requires participants to
alternate rapidly between repeating spatial spans of increasing length and
symmetry judgments for patterns of increasing complexity. The outcome
measure used in this study was the total number of correctly completed
trials.
We found no significant baseline differences for working memory and
executive function performance in the pretraining session between the
two groups: digit span forwards (t(38) ⫽ ⫺0.67, p ⫽ 0.51), digit span
backwards (t(38) ⫽ ⫺0.01, p ⫽ 0.99), spatial span (t(38) ⫽ ⫺0.97, p ⫽
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
0.34), double trouble (t(38) ⫽ 0.69, p ⫽ 0.49), grammatical reasoning
(t(38) ⫽ ⫺0.97, p ⫽ 0.34), tree task (t(38) ⫽ ⫺1.54, p ⫽ 0.13), odd one out
(t(38) ⫽ ⫺0.37, p ⫽ 0.71), self-ordered search (t(38) ⫽ ⫺1.90, p ⫽ 0.07),
and automated symmetry span task (t(38) ⫽1.18, p ⫽ 0.25).
MRI data acquisition. We captured multiple attributes of brain tissue
structure through the use of different types of MRI scans acquired on a 3T
HDx MRI system (General Electric Medical Systems) using an eight
channel receive-only RF head coil. The MRI sessions were interleaved for
the adaptive and nonadaptive groups to avoid any potential confounds
between the experimental conditions and scanner-related changes in
data acquisition, such as scanner drift effects.
Specifically, we combined well established (although nonspecific) diffusion tensor MRI metrics with both MR relaxometry-based metrics
(Deoni et al., 2008) and metrics derived from advanced models of diffusion (Assaf and Basser, 2005). Relaxometry-based quantitative metrics,
including the “myelin water fraction,” have been shown to have high
qualitative and quantitative correspondence with the degree of myelination, as measured by more direct histological means, and thus can be
used as an indirect measure of myelin (MacKay et al., 1994; Mottershead
et al., 2003; Schmierer et al., 2008; Hurley et al., 2010; Deoni et al., 2011,
2012; Kitzler et al., 2012; Kolind et al., 2012). Complementing these
relaxometry-based markers, the advanced models of diffusion (Assaf and
Basser, 2005) yield proxy estimates of axonal density.
Diffusion-weighted data were acquired using a cardiac-gated singleshot spin-echo EPI sequence using the following parameters: 60 axial
slices; slice thickness: 2.4 mm; echo time (TE): 89.1 ms; number of diffusion directions 60 (using an optimized gradient vector scheme) (Jones
et al., 1999); b-value: 1200 s/mm 2; six non– diffusion-weighted scans;
FOV: 230 mm ⫻ 230 mm; acquisition matrix: 96 ⫻ 96 (total acquisition
time TA ⬃30 min depending on their heart rate). High-resolution T1weighted anatomical images were acquired using a fast-spoiled gradient
recalled echo (FSPGR) sequence (172 slices; slice thickness: 1 mm; acquisition matrix: 256 ⫻ 256; TE: 2.9 ms; TR: 7.8 ms; flip angle: 20°; FOV: 230
mm ⫻ 230 mm; TA: 7 min). Quantitative maps indexing axonal morphology were acquired using the CHARMED protocol (Assaf and Basser,
2005) (slice thickness: 2.4 mm; TE: 126 ms; TR: 17,000 ms; 45 gradient
orientations distributed on 8 shells)/(Santis et al., 2014) (maximum
b-value: 8700 s/mm 2; FOV: 230 mm ⫻ 230 mm, acquisition matrix: 96 ⫻
96; TA ⫽ 13 min). Finally, maps of putative indices of myelin were
acquired using the mcDESPOT protocol (Deoni et al., 2008) that comprised spoiled gradient recalled (SPGR) acquisitions: TE: 2.1 ms; TR: 4.7
ms; flip angles: (3, 4, 5, 6, 7, 9, 13, 18°); and balanced Steady-State Free
Precession (bSSFP) acquisitions: TE: 1.6 ms; TR: 3.2 ms; flip angles:
(10.6, 14.1, 18.5, 23.8, 29.1, 35.3, 45, 60°); spatial resolution: 1.7 mm
isotropic; TA: 12 min. bSSFP acquisitions were repeated with and without 180° RF phase alteration to remove SSFP banding artifacts, B0- and
B1-induced errors in the derived myelin water fraction estimates (Deoni
et al., 2011).
Analyses of neuroimaging data. Eleven different kinds of networks were
generated using the subject’s diffusion MRI, CHARMED, mcDESPOT,
and T1-weighted data (Fig. 1). A network was defined as a set of nodes
(denoting anatomical regions of the parcellation scheme) and interconnecting edges (denoting tractography-reconstructed fiber trajectories
that interconnect the nodes). The reconstructed graphs were all undirected as tractography does not differentiate between efferent and afferent fibers. Moreover, different quantitative white metrics were assigned
to the edges of the graph, resulting in weighted graphs. We now describe
the processing steps starting from analyses of the neuroimaging data to
computation of the topological metrics of the graph.
The diffusion-weighted data were corrected for distortions induced
by the diffusion-weighted gradients, artifacts due to head motion and
due to the EPI-induced geometric distortions by registering each image
volume to the high-resolution T1-weighted anatomical images (Irfanoglu et al., 2012), with appropriate reorientation of the encoding vectors
(Leemans and Jones, 2009). A two compartment model using the Free
Water Elimination approach (Pasternak et al., 2009) was then fitted to
derive maps of FA, mean diffusivity, radial diffusivity, and axial diffusivity (Pierpaoli et al., 1996), corrected for partial volume contamination of
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
J. Neurosci., April 6, 2016 • 36(14):4056 – 4066 • 4059
Freesurfer analyses. FreeSurfer (http://surfer.
nmr.mgh.harvard.edu) was used for cortical
reconstruction and volumetric segmentation
reconstruction of the brain’s surface to compute cortical thickness using a semiautomated
approach described in detail previously (Jovicich et al., 2009; Fischl, 2012), with use of additional computing resources from the high
performance computing TIER1 cluster at the
University of Gent (http://www.ugent.be/
hpc/). Images were processed automatically
using the FreeSurfer longitudinal stream (Reuter et al., 2012). Specifically, an unbiased
within-subject template space and image were
created using robust, inverse consistent registration. Several processing steps, such as skull
stripping, Talairach transforms, atlas registration, spherical surface maps, and parcellations,
were then initialized with common information from the within-subject template, resulting in increased reliability and statistical
power. Coregistered and segmented images
were inspected visually for quality assurance
purposes. Automated cortical parcellation and
ROI labeling were performed to obtain (sub)cortical volume measures for the nodes of inFigure 1. Multimodal characterization of the tissue structure, combining diffusion tensor MRI-based indices (including FA) with terest (see below).
Nodes. ROIs were defined based on previous
those derived from multicomponent relaxometry as putative measures of myelin (MWF), intrinsic relaxation rates R1 and R2, and
fMRI
studies in children, young adults, and
the TRF derived from the CHARMED pipeline as a putative measure of axonal density, together with Freesurfer-derived estimates
older adults undergoing Cogmed training (e.g.,
of volume derived from the FSPGR T1-weighted anatomical data.
Olesen et al., 2004; Klingberg, 2010; Ullman et
al., 2014). Thirty regions from the automated
anatomical labeling atlas (AAL) (TzourioCSF, together with a map of the tissue volume fraction in each voxel
Mazoyer et al., 2002) were selected to define the nodes of the network
(Metzler-Baddeley et al., 2012).
(Fig. 2). The areas constituting this network included the inferior and
CHARMED data were corrected for motion and distortion artifacts
superior parietal cortex, supramarginal gyrus, caudal and rostral middle
according to Ben-Amitay et al. (2012) and were corrected for CSFdorsolateral prefrontal cortex, superior frontal cortex, inferior ventrolatpartial volume contamination with the Free Water Elimination
eral prefrontal cortex (pars opercularis, pars triangularis, and pars ormethod. The number of distinct fiber populations (1, 2, or 3) in each
bitalis), insula, and anterior cingulate cortices. In addition, subcortical
et
al.,
voxel was determined using a model selection approach (Santis
regions of the basal ganglia (i.e., caudate, putamen, and globus pallidum)
2014), and the total restricted fraction (TRF, i.e., the fraction of the
as well as the thalamus were included in the analyses. Each ROI of the
signal assigned to restricted diffusion) was calculated per voxel with
AAL template represented a node of the network.
in-house software coded in MATLAB (The MathWorks) (Santis et al.,
White matter tractography networks. The connection strengths for the
2014).
white matter networks were defined by the quantitative metrics derived
The spoiled gradient recalled echo (SPGR) and balanced steady-state
from the diffusion tensor MRI, CHARMED, and mcDESPOT pipelines,
free precession (bSSFP) images acquired as part of the mcDESPOT pipeincluding the following: (1) diffusion MRI measures of average FA, tissue
line (Deoni et al., 2008) were corrected for motion using the FMRIB
volume fraction, the number of reconstructed fibers, axial diffusivity,
Linear Image Registration Tool (Smith et al., 2001) to align all images to
and the inverse of the mean and radial diffusivity; (2) myelin water fracthe first in the acquisition series. The mcDESPOT model was fitted to the
tion and the inverse of the intrinsic relaxation times T1 (R1) and T2 (R2)
data using in-house software coded in C⫹⫹ (Deoni et al., 2008) to
from the mcDESPOT pipeline; and (3) the total restricted fraction from
obtain maps of the myelin water fraction and of the intrinsic relaxation
the CHARMED pipeline (Fig. 1). As a result, for each participant, there
times T1 and T2 (subsequently used to derive R1 and R2 maps). All quanwere 10 different kinds of weighted white matter networks, each of which
titative maps were coregistered to the T1-weighted anatomical images.
was represented by a symmetric 30 ⫻ 30 connectivity matrix.
The TRF maps (derived from CHARMED) were coregistered using the
Gray matter covariance networks. The gray matter volumes of the 30
Elastix registration toolbox (Klein et al., 2010), whereas the myelin water
anatomical regions of interest (as described above) were used to confraction (MWF), R1, and R2 maps (derived from mcDESPOT) were
struct structural correlation networks. For each group and each time
coregistered to the T1-weighted anatomical image using the FMRIB nonpoint, a 30 ⫻ 30 correlation matrix R was generated with each entry rij
linear registration tool FNIRT.
defined as the Pearson correlation coefficient between the gray matter
Whole-brain tractography was performed for each participant and
volume measures of regions i and j, across participants (He et al., 2007;
each time point in the participant’s native space using the damped
Bernhardt et al., 2011; Fan et al., 2011).
Richardson-Lucy algorithm (Dell’acqua et al., 2010), which (in contrast
Graph theoretical network analysis. We quantified measures of network
to diffusion tensor MRI) allows for recovery of multiple fiber orientaintegration (characteristic path length) and segregation (clustering) for
tions within each voxel. The tracking algorithm estimated peaks in the
each network (Rubinov and Sporns, 2010). The characteristic path length
fiber orientation density function (fODF) using each voxel as a seed point
L of a network is the average shortest path (distance) between all pairs of
and propagated in 0.5 mm steps along these axes reestimating the fODF
nodes in the network. It is defined as follows:
peaks at each new location (Jeurissen et al., 2010). Tracks were terminated if the fODF amplitude fell ⬍0.05 or the direction of pathways
changed through an angle ⬎45° between successive 0.5 mm steps. This
d ij
1
j 僆N j⫽i
procedure was then repeated by tracking in the opposite direction from
L⫽
n i僆N n ⫺ 1
the initial seed point.
冘
冘
4060 • J. Neurosci., April 6, 2016 • 36(14):4056 – 4066
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
Figure 2. Cortical and subcortical regions (30 in total) as defined anatomically by the AAL template image in standard stereotaxic space.
where dij is the shortest path length (distance) between nodes i and j. The
global efficiency (Latora and Marchiori, 2001) is the average inverse
shortest path length in the network and is inversely related to the characteristic path length. In other words, networks with a small average
characteristic path length have higher efficiency than those with large
average characteristic path length.
The clustering coefficient of a node is a measure of the number of edges
that exist between its nearest neighbors and is quantified by counting the
numbers of triangles, t, formed around a node (Onnela et al., 2005;
Opsahl and Panzarasa, 2009). The clustering coefficient C of the network
is the average clustering coefficient across all nodes and is quantified as
follows:
C⫽
冘
1
2t i
n i僆N k i 共 k i ⫺ 1 兲
where ki is the number of connections (degree) for node i and ti is the
number of triangles around node i.
Statistical analysis. It is important to recognize that the various cognitive measures (tasks for pretraining and post-training) may not be
independent and could well all be impure measures of overlapping
latent constructs. To account for this, an exploratory principal component analysis was run within the complete dataset of the pretraining cognitive data (including the subjects that did not complete the
training; i.e., N ⫽ 46), to obtain composites to reduce the number of
measures before conducting further analyses (ANOVAs and correlation analyses) on those composites (see below). Here, a procedure was
used with Varimax rotation of the factor matrix to minimize the
complexity of the components, whereby each factor has a small number of large loadings and a large number of zero (or small) loadings.
Factor pattern matrices were identified using the Kaiser criterion (i.e.,
factors with Eigenvalues ⬎1 were regarded significant). The factor
loadings reflect the strength of each variable in defining the factor
with negative loadings indicating that a variable related negatively to
the other components. Per convention, variables were included when
their loading exceeded a value of 0.5. This principal component analysis revealed three significant behavioral components that together
accounted for 59% of the total variance (Table 2). More specifically,
all of the tasks in which information had to be actively maintained in
short-term memory, for example, the automated symmetry span task,
the spatial span task, and the odd one out task loaded heavily on the
first component. This complex span working memory factor accounted for 34% of the variance. The second component (accounting
for 13% of the variance) was associated with tasks involving a verbal
component, including the double trouble task, digit span tasks, and
the grammatical reasoning task. Tasks requiring general reasoning,
including the Hampshire tree task and the self-ordered spatial span,
loaded heavily on the third component, accounting for 12% of the
variance. Second, we computed composite scores by converting the
raw test scores of these tasks to z-scores. Then, we summed the
z-scores for each component per time point to create a composite for
that time point. Finally, we used the composite scores as dependent
variables in a 3 ⫻ 2 ⫻ 2 (factor ⫻ group ⫻ time) repeated-measures
ANOVA to investigate training effects.
Interaction effects between group and time for the graph metrics
were analyzed using the longitudinal plugin of the Graph Analysis
Toolbox (Kesler et al., 2013). Specifically, each edge weight was first
normalized by the mean edge weight across the network, and graph
metrics were computed for the normalized networks. A nonparametric permutation test with 1000 repetitions was then used to test the
statistical significance of the effects of time course and group (adaptive and nonadaptive group) on the graph metrics (Bassett et al., 2008;
Hosseini et al., 2012). In each permutation, the residuals of each
participant were randomly assigned to one of the two groups so that
each randomized group had the same number of subjects as in the
original groups. Finally, the actual difference in the slope between the
original groups was compared with the distribution of difference in
slope between randomized groups (obtained through the permutation procedure) to obtain the p value. The same permutation procedure was used to test the significance of the differences in regional
network measures. In this step, we compared regional clustering coefficient for the networks thresholded at the minimum density in
which the networks of both groups were not fragmented. We obtained
false discovery rate (FDR)-corrected p values as measures of significance for the regional measures comparisons. In the present study,
the p values reported for regional differences between groups are
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
J. Neurosci., April 6, 2016 • 36(14):4056 – 4066 • 4061
Figure 3. Cognitive changes for the adaptive and nonadaptive group. Performance improvements in the adaptive group were significant on the complex working memory factor (Component 1)
and the verbal factor (Component 2). *p ⬍ 0.05 ( post hoc test). ***p ⬍ 0.001 ( post hoc test). Error bars indicate SE.
FDR-corrected for multiple comparisons (30 comparisons, each corresponding to one of the anatomical nodes).
Finally, Pearson product correlation coefficients were calculated
between the change in global efficiency of the network and (1) the
three composite scores derived from the principal component analysis of the cognitive measures and (2) the scores from the CogMed
measures. FDR corrections for multiple comparisons were made.
Results
Training-related changes in tasks of working memory and
executive function tasks
Using a 3 ⫻ 2 ⫻ 2 repeated-measures ANOVA, analysis of the
composite scores revealed a main effect of time (F(1,38) ⫽ 96.59,
p ⬍ 0.001) and group (F(1,38) ⫽ 5.94, p ⫽ 0.02). The main effect of
factor was not significant. No significant interaction effect was
found between factor and group. However, the time ⫻ group
interaction (F(1,38) ⫽ 12.19, p ⬍ 0.001) and the time ⫻ factor
interaction (F(2,37) ⫽ 10.07, p ⬍ 0.001) were significant. The
three-way interaction between factor, group, and time was also
significant (F(2,37) ⫽ 4.07, p ⫽ 0.021). Post hoc t testing showed a
superior performance on the complex working memory score
factor ( p ⫽ 0.001) and the verbal factor ( p ⫽ 0.028) in the posttraining session for the adaptive group compared with the nonadaptive group, as shown in Figure 3.
Graph theoretical network analysis of the working memory
training effects
We found a significant group ⫻ time interaction effect when the
network edges were weighted by the intrinsic longitudinal relaxation rate, R1 (1/T1), derived from the mcDESPOT protocol
(F(1,38) ⫽ 4.50, p ⬍ 0.04; as shown in Fig. 4). The post hoc twosided t tests demonstrated an increase in global efficiency (i.e.,
more global integration) between nodes of the network in the
adaptive group from the presession to the postsession ( p ⬍ 0.04).
This group difference was not seen at pretest.
Marginal significant interaction effects were observed for the
global efficiency of the graphs weighted by diffusion-derived parameters, including FA ( p ⫽ 0.057), 1/mean diffusivity (MD)
( p ⫽ 0.063), axial diffusivity ( p ⫽ 0.052), tissue volume fraction
( p ⫽ 0.056), 1/radial diffusivity ( p ⫽ 0.061), and number of
streamlines ( p ⫽ 0.073). No significant interaction effects were
observed for the graph weighted by the total restricted fraction
derived from the CHARMED protocol ( p ⫽ 0.15) or in the graph
derived from the covariance of gray matter volumes ( p ⫽ 0.35).
Regional analyses
Clustering coefficient was evaluated at the nodal level, to identify
the nodes in the network that are responsible for the working
memory training effects. The FDR was used to correct for multiple comparisons. The clustering coefficient of the right anterior
rostral cingulate gyrus showed a significant (group ⫻ time) interaction for the volume-weighted networks (F(1,38) ⫽ 13.58, p ⬍
0.05, FDR-corrected). Moreover, for the intrinsic relaxation rate,
R2, of the mcDESPOT protocol, we observed a significant
(group ⫻ time) interaction effect for the clustering coefficient of
the right inferior ventrolateral prefrontal cortex (F(1,38) ⫽ 9.94,
p ⬍ 0.05, FDR-corrected; Fig. 5).
The post hoc two-sided t tests of the clustering coefficient of
the right anterior cingulate gyrus (volume-weighted networks) and the right inferior ventrolateral prefrontal cortex
(R2-weighted networks) revealed a significant increase (i.e.,
more functional segregation) from the pretraining to the posttraining session in the adaptive group ( p values ⬍0.05). This
difference was not apparent in the baseline session ( p values
⬎0.10).
Correlations between changes in global efficiency and
improved performance on Cogmed tasks and cognitive tasks
The analyses of correlations between the changes in global
efficiency from pretraining to post-training and the composite
scores of the behavioral parameters showed little direct association between changes in structural network metrics and the
improved performance on cognitive tests or Cogmed tasks.
Using an exploratory uncorrected threshold of p ⬍ 0.05, we
observed correlations between the changes in Cogmed tasks
and changes in global efficiency of the R1-weighted networks
(data room, r ⫽ 0.46; rotating dots, r ⫽ 0.43), pairing better
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
4062 • J. Neurosci., April 6, 2016 • 36(14):4056 – 4066
Figure 4. Difference scores (postassessment ⫺ preassessment) in global efficiency of the different weighted networks. *An increase in global efficiency following working memory training was
best captured by the network weighted by the longitudinal relaxation rate (R1). TVF, Tissue volume fraction; AD, axial diffusivity; RD, radial diffusivity.
working memory performance on the Cogmed with higher
efficiency of information transfer (i.e., more global integration). None of the correlations survived the necessary correction for multiple comparisons.
Correlations between global efficiency across the
different parameters
The scores of global efficiency of the networks constructed with
different metrics as “connection strengths” were highly intercorrelated at baseline and thus represent nonindependent observations (Table 3, lower triangle). For example, global efficiency of
the network whose connections strengths were defined by the
quantitative relaxation rate R1 (1/T1) derived from the
mcDESPOT pipeline correlated strongly with global efficiency of
the network weighted by the TRF derived from the CHARMED
pipeline (r ⫽ 0.55, p ⬍ 0.01). There were also strong correlations
between the difference scores (post-training vs pretraining) in
global efficiency of the networks constructed with the different
weights. Importantly, the difference scores in global efficiency of
the networks weighed by R1 were not correlated significantly with
difference scores in efficiency of the network weighted by the TRF
metric (r ⫽ 0.42, p ⫽ 0.063). Similarly, difference scores in efficiency of the R1-weighted network did not correlate significantly
with difference scores of the MWF-weighted network (r ⫽ 0.44,
p ⫽ 0.051). Thus, although all metrics correlate at baseline, the
reduction in correlation post-training suggests that the white
matter network undergoes changes that are more sensitively
detected with R1 and that these different metrics index different aspects of white matter microstructure (Table 3, upper
triangle). Important to note, these reductions in correlation
were not significant using the Fisher r-to-z transformation (all
p values ⬎0.10).
Discussion
This is the first study to explore training-induced changes in the
structural connectome using a well-controlled design to examine
cognitive training with up-to-date neuroimaging methods. Our
findings showed that improved performance on tasks of working
memory (i.e., near-transfer effects) and executive function tasks
(i.e., far-transfer effects) occur alongside an increase of global
efficiency (i.e., more global integration) of the network in the
adaptive group. More importantly, the relaxation rate-weighted
networks provided enhanced sensitivity to training-induced
white matter changes compared with other weighted networks.
Furthermore, the increased efficiency was related to improved
performance on Cogmed tasks.
Near-transfer effects together with more general effects on
reasoning and inhibition
Significant practice-induced improvements in working memory
tasks and executive functioning tests were observed in the adaptive group. Intensive adaptive training of working memory has
been shown to enhance individual working memory capacity in
healthy adults, in older adults, and in clinical populations, such as
children with ADHD and stroke patients (for review, see Takeuchi et al., 2010). Here, we made use of an internet-based training
program, originally developed by Klingberg et al. (2002, 2005) for
children with ADHD, which enabled storage of training information at the trial-by-trial level and allowed us to include a nonadaptive group. The effective training time was 40 –50 min per
day, 5 d a week for 8 weeks (totaling ⬃30 h).
Specifically, we found that performance improvements in the
adaptive group were significant on two components, which
loaded on tasks of working memory span (i.e., the digit span,
spatial span, and automated symmetry span task), reasoning (i.e.,
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
J. Neurosci., April 6, 2016 • 36(14):4056 – 4066 • 4063
Figure 5. Change in clustering coefficient from preassessment (left) to postassessment (right). Size of the ROIs (spheres) represents the clustering coefficient. Magenta represents the node in the
network that is significant after FDR correction.
Table 3. Correlations between global efficiency of networks weighted by different metrics, including (1) diffusion tensor MRI measures of average FA, TVF, the number of
reconstructed fibers (tracts), axial diffusivity (AD), the inverse of the mean (MD), and radial diffusivity (RD); (2) MWF and the inverse of the intrinsic relaxation times T1 (R1)
and T2 (R2) derived from mcDESPOT; and (3) the TRF of the CHARMED dataa
Weights
Group
AD
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
Adaptive
Nonadaptive
FA
TRF
TVF
1/MD
MWF
1/RD
TRACTS
R1
R2
a
AD
FA
TRF
TVF
1/MD
MWF
1/RD
TRACTS
R1
R2
0.977*
0.995*
0.851*
0.972*
0.873*
0.979*
0.995*
0.996*
0.983*
0.994*
0.860*
0.978*
0.996*
0.999*
0.975*
0.996*
0.848*
0.977*
0.998*
0.999*
0.847*
0.971*
0.863*
0.981*
0.978*
0.990*
0.856*
0.972*
0.849*
0.974*
0.996*
0.999*
0.975*
0.996*
0.848*
0.977*
0.998*
0.999*
1.000*
1.000*
0.849*
0.974*
0.994*
0.998*
0.975*
0.994*
0.850*
0.977*
0.994*
0.997*
0.997*
0.999*
0.849*
0.973*
0.997*
0.999*
0.784*
0.994*
0.777*
0.994*
0.423
0.984*
0.788*
0.993*
0.788*
0.995*
0.442
0.983*
0.788*
0.995*
0.782*
0.995*
0.896*
0.979*
0.896*
0.983*
0.632*
0.984*
0.906*
0.976*
0.904*
0.980*
0.643*
0.993*
0.904*
0.980*
0.901*
0.982*
0.960*
0.991*
0.976*
0.921*
0.924*
0.995*
0.977*
0.929*
0.997*
0.974*
0.924*
0.999*
0.898*
0.907*
0.970*
0.904*
0.901*
0.997*
0.974*
0.924*
0.999*
1.000*
0.901*
0.994*
0.976*
0.924*
0.996*
0.998*
0.903*
0.998*
0.765*
0.756*
0.553*
0.777*
0.776*
0.545*
0.776*
0.777*
0.836*
0.846*
0.692*
0.856*
0.855*
0.700*
0.855*
0.856*
Lower triangle represents pretraining (total group). Upper triangle represents difference scores.
*Correlation is significant at the 0.01 level (two-tailed).
0.954*
4064 • J. Neurosci., April 6, 2016 • 36(14):4056 – 4066
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
Working memory training modified the
structural connectome
Our key graph metrics showed consistent increases in the adaptive group between the pretraining and post-training assessments
indicating that mean network connectivity increased significantly in the context of adaptive training of working memory.
Specifically, increases in global efficiency suggest that training
resulted in increased integration in the examined network.
Similarly, increases in clustering coefficient in the adaptive
group, suggest that the network in the adaptive group became
more closely connected at a regional level following the 8 week
training regimen. This was especially the case for the right anterior cingulate gyrus and the right inferior ventrolateral prefrontal
cortex. The right anterior cingulate gyrus is often associated with
attentional control and mental effort (Buschkueh et al., 2012).
The right inferior ventrolateral prefrontal cortex is hypothesized
to play an important role in attentional orienting processes (Corbetta et al., 2008). As task difficulty was adaptively increased, the
Cogmed tasks required more attentional control and deployment
with training, resulting in increase in functional segregation in
the adaptive group.
Our results are consistent with a previous EEG study of Langer
et al. (2013), who reported increased small-worldness within
frontoparietal regions of the functional network in high performers with working memory training. The combined interpretation
of this previous finding with the present result that network metrics increase in the adaptive group indicates that graph theoretical
analysis can capture the dynamics of both the functional and
structural connectome.
metrics have used diffusion tensor MRI-derived measures that
reflect the local anisotropy or overall rate of diffusion (e.g., FA
and MD, respectively). These FA- or MD-weighted connectomics should be interpreted with caution. Such metrics are sensitive to manifold properties of tissue (e.g., myelination, axonal
density, axon diameter, intravoxel orientational dispersion) that
cannot be reliably distinguished with diffusion tensor MRI (Jones
et al., 2013). Other definitions of edge weight, such as the level of
myelination as inferred through the magnetization transfer ratio,
have been used previously (van den Heuvel et al., 2010). However, multicomponent relaxometry metrics (such as those derived from the mcDESPOT protocol used here) have been shown
to have greater myelin-specificity than diffusion anisotropy or
magnetization transfer imaging (Madler et al., 2008; Vavasour et
al., 2011).
Our results showed that changes in global efficiency in the
adaptive group could be demonstrated for the network weighted
by the quantitative R1 relaxation rate. Moreover, a significant
interaction effect was reported for the clustering coefficient when
using R2 to weight the network. No significant differences were
found for the network weighted by the MWF. These results suggest that these different relaxometry-derived metrics inform on
different complementary aspects of tissue microstructure and
biochemical features (Alexander et al., 2011; Deoni et al., 2012,
2015). Both T1 and T2 are affected by changes in water, lipid, and
protein content. T2 is also sensitive to changes in iron within the
oligodendrocytes. MWF is thought to be more specific to changes
in lipid myelin content. The results shown herein, specifically
the lack of significant training-related changes of MWF versus
R1 or R2 alterations in the adaptive group, suggest that iron,
water, lipid, and protein content is altered with working memory training.
Compelling evidence from previous studies (Ortiz et al., 2004;
Holmes-Hampton et al., 2012) suggests that iron accumulation
by oligodendrocytes may contribute to training-induced changes
in brain white matter (Zatorre et al., 2012). These alterations in
iron are essential for myelin production and can influence the
cholesterol and lipid biosynthesis. This induced myelin formation is probably a process of myelin remodeling in healthy adults,
whereby additional myelin internodes are added to partially myelinated axons in such a way that the total number of myelinating
cells increases without a concomitant increase in the total length
of myelin sheath (Young et al., 2013; Wang and Young, 2014).
Thus, iron accumulation by oligodendrocytes in the preparation
of myelination may have caused the changes in the R1 and R2
relaxation rates over 2 months of training. Further exploratory
analyses revealed a high correlation at baseline between global
efficiency of the networks weighted by R1 and by the TRF, but no
correlation in the difference scores in the adaptive training group.
In short, we suggest that our observed structural changes in the
healthy young adults are underpinned by subtle changes in microscopic structures, such as oligodendrocytes, rather than
changes in the number of axons or axon diameter.
Multicomponent relaxometry approach as a sensitive weight
To our knowledge, this is the first time that differently weighted
structural brain networks were compared, in which the edges of
the brain graphs had continuously variable weights representing
potentially more specific markers of white matter than diffusiontensor MRI indices or number of streamlines. Until recently, the
question about which quantitative metric would be most useful
as a weight to probe connectivity remained open (Fornito et al.,
2013). The majority of connectome studies using weighted graph
Relationship between changes in the structural connectome
and behavior
No correlations were found between improvements on the cognitive tasks over the training period and degree of change of
graph metrics. We only observed a positive correlation between
change in the Cogmed tasks and change in global efficiency in the
adaptive training group. This suggests that training-induced improvements of Cogmed tasks were associated with increases in
global efficiency. Our findings further support associations be-
odd one out, grammatical reasoning), and inhibition (i.e., double
trouble task). In other words, the training led to improvement on
tests of the same domain (near-transfer) and generalized to tasks,
such as (non)verbal reasoning and inhibition (far-transfer). The
near-transfer results have also been confirmed in several recent
meta-analyses of working memory training using Cogmed or
other training programs (e.g., Melby-Lervåg and Hulme, 2013;
Peijnenborgh et al., 2015; Schwaighofer et al., 2015; SpencerSmith and Klingberg, 2015; Cortese et al., 2015). Moreover, our
results are consistent with previous studies that have demonstrated transfer to other cognitive constructs that were not part of
the training program (Klingberg et al., 2002, 2005; Westerberg et
al., 2007; Holmes et al., 2009). It is important to note that, in the
present study, we did not observe a general training effect across
all three factors (i.e., our training effects were selective to some
outcome measures). We note that, in the previous reports, the
participants were either very young or from a clinical cohort, as
opposed to our cohort of fully mature and healthy adults (mean
age: 26.5 years). It is possible, therefore, that there is less capacity
for general training effects in our cohort than in clinical groups/
young participants. It is also possible that our study lacked the
statistical power to detect the more general training effects (Au et
al., 2014; Karbach and Verhaeghen, 2014).
Caeyenberghs, Metzler-Baddeley et al. • Dynamics of the Connectome
tween working memory improvements over time and changes in
graph metrics, which provides potential new insights into the
mechanisms underpinning training-induced plasticity that may
drive memory improvement in clinical populations.
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